Distributed photovoltaic, wind power and load power balancing strategy utilizing energy storage adjustment

Abstract

The invention discloses a distributed photovoltaic, wind power and load power balancing strategy utilizing energy storage adjustment, which comprises model construction and specifically comprises the following steps of: a, integrating constraint sets with various requirements: (1) power distribution network flow constraints relating to a plurality of nodes; (2) restraining charging and discharging power of the energy storage power station; (3) carrying out regional charging and discharging power balance constraint; (4) continuously constraining the capacity of the energy storage power station; (5) constraint of charging and discharging power of the user group; (6) considering photovoltaic and wind power access of users in the region; and b, constructing according to an energy storage regulation and control strategy of economical efficiency and stability. On the basis of power constraint, energy storage capacity constraint and the like, the access of renewable energy sources is considered, a shared energy storage power station optimization scheduling model is established, and mixed linear integer optimization is carried out by taking the minimum regional daily operation cost as a target function. In addition, the constraint is simplified by selecting a proper method so as to solve the power system expansion planning problem quickly and accurately, and therefore the stability and economical efficiency of a power grid are improved.

Description

Technical Field

[0001] The present invention relates to a distributed photovoltaic, wind power and load power balancing strategy utilizing energy storage regulation. Technical Background

[0002] Existing technologies mainly focus on utilizing the uncertainty factors brought by renewable energy such as wind and solar to regulate energy storage, mainly including those based on uncertainty mathematical theory and scenario analysis technology. In these studies, existing solutions usually need to find a balance between the stable operation requirements of the power grid and the economic consumption of new energy output to achieve minimum energy consumption and maximum efficiency.

[0003] In the process of finding this balance point, it is often necessary to take into account factors such as the output time, output power, and fluctuation range of new energy, and comprehensively consider factors such as the load condition of the power grid and the price of electricity for optimization, so as to achieve the goal of reducing electricity consumption or improving the efficiency of the use of new energy power generation capacity.

[0004] In addition, some studies also focus on how to find a suitable algorithm to solve the model in order to obtain the optimal regulation scheme for energy storage. This usually requires comparing different algorithms and solving, analyzing and verifying the established models.

[0005] Existing studies have tracked the volatility of renewable energy installations through energy storage regulation methods, improving the system's ability to cope with uncertain events. However, the above studies are all planned from the perspective of uncertainty in power generation, and the requirements for economic efficiency are not in place. In fact, as far as current research is concerned, economic efficiency generally focuses on operating costs, electricity purchase costs, and electricity sales prices. Only a few literature studies have considered the impact of equipment loss at the same time, and most of them have complex algorithms. There are few strategies with simple algorithms and comprehensive considerations.

[0006] Therefore, based on the existing technical solutions, there is an urgent need for an energy storage regulation strategy that comprehensively considers the power generation load balance and the economic requirements of the power system, and effectively achieves the balance between system stability and economy through constraint transformation. The power balance regulation strategy will neither lead to poor economy due to simplicity nor cause the system operation risk to be out of control, so as to achieve the best economic benefits. This ensures the stability and economy of the power system when a large number of new energy units are connected.

[0007] In recent years, my country has vigorously expanded the development and utilization of new energy, and wind power and photovoltaics have been increasingly incorporated into the power system, resulting in a huge change in the energy structure. However, integrating these time-varying and volatile new energy units into the power grid has increased the complexity of operations and posed the following challenges to real-time power balance.

[0008] First, in terms of grid dispatching and operation, the number of distributed new energy units is huge and the layout is scattered, with poor visibility and controllability. The volatility of their output has a significant impact on grid dispatching and operation. For example, on the distribution network side, a large-scale distributed photovoltaic power generation at noon will cause voltage increase and power flow reversal at the end of the distribution network. At the same time, the volatility of its output also increases the uncertainty of terminal load. Second, in terms of economic efficiency, the subsidies for distributed units are continuously reduced and will eventually achieve grid parity, and the investment attractiveness is declining, which puts its future sustainable development to the test. With the development of photovoltaic power generation technology and the continuous decline in manufacturing costs in various links of the industrial chain, more and more countries are gradually reducing fixed electricity price compensation, which puts the investment attractiveness of distributed photovoltaics to the test.

[0009] The current infrastructure is not sufficient to support this level of renewable energy integration and needs to be further developed and strengthened. At the same time, power market reform aims to promote the evolution of the power system towards a market-oriented one. Therefore, establishing a storage regulation strategy that meets economic needs and the uncertainty of renewable energy is essential to ensure that the grid operates efficiently, environmentally friendly and safely.

[0010] In order to cope with the uncertainty of power flow caused by renewable energy and load fluctuations, a variety of management methods have been proposed, including optimizing the transmission network, building backup units, increasing energy storage, etc., to improve reliability and manage the risks caused by overload and congestion.

[0011] However, how to quickly model the uncertainty of renewable energy generation and effectively improve the safety and economy of the power grid is still a key problem that we need to solve. This application aims to solve this problem and proposes a new power balancing strategy to provide a new solution for the optimization of power systems with high penetration of renewable energy generation. Summary of the invention

[0012] The present invention aims to overcome the defects of the prior art and proposes a storage control strategy that takes into account the access of distributed units. It is a new method for absorbing new energy power generation and can solve the volatility balance problem of new energy high penetration power grids.

[0013] The technical solution to achieve the above purpose is: a distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, including model construction, which is characterized by: specifically including the following:

[0014] a. Constraint set integrating multiple requirements:

[0015] (1) Power flow constraints of distribution networks involving multiple nodes;

[0016] (2) Constraints on charging and discharging power of energy storage power stations;

[0017] (3) Regional charging and discharging power balance constraints;

[0018] (4) Continuity constraints on energy storage power station capacity;

[0019] (5) Charging and discharging power constraints of user groups;

[0020] (6) Consider the access of users in the region to photovoltaic and wind power;

[0021] b. Construct energy storage control strategies based on economy and stability.

[0022] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, the power flow constraints of the distribution network involving multiple nodes are:

[0023]

[0024] Where: P i and Q i is the active and reactive power injected at node i, U i , U j is the voltage amplitude of nodes i and j, G i,j , B i,j and θ i,j are the conductance, susceptance and voltage-current phase difference between nodes i and j respectively.

[0025] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, the energy storage power station charging and discharging power constraints are:

[0026]

[0027] Where: P cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage station, P cha (t)·P dis (t) = 0 means that charging and discharging will not occur simultaneously.

[0028] In the above-mentioned distributed photovoltaic, wind power and load power balance strategy using energy storage regulation, the regional charging and discharging power balance constraint is:

[0029]

[0030] Where: P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i P (t) is the discharge power of the energy storage power station to the i-th user in period t, and N is the number of users in the area. cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage power station.

[0031] Ignoring transmission losses, the difference in charging and discharging power among users in the region is equal to the difference in charging and discharging power of the shared energy storage power station.

[0032] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, the energy storage power station capacity continuity constraint is:

[0033]

[0034] Where: E(t) is the state of charge of the shared energy storage power station, E min and E max are the minimum and maximum values ​​of charge, P cha (t) and P dis (t) are the charge and discharge power, η cha and η dis They are the charging and discharging efficiency of the energy storage power station.

[0035] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, the user group power balance constraint is:

[0036] P purchase,i (t)+P dis,i (t)+P PV,i (t)+P wind,i (t) = P cha,i (t)+P load,i (t) (17)

[0037] Where: P purchase,i (t) is the power purchased by the i-th user in period t, P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i (t) is the discharge power of the energy storage power station to the i-th user in period t. PV,i (t) is the photovoltaic output of the i-th user, P wind,i (t) is the wind power output of the ith user, P load,i (t) is the load of the ith user.

[0038] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, the user group charging and discharging power constraints are:

[0039]

[0040] Where: P cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage power station, The maximum charging power of the user to the energy storage power station, is the maximum discharge power,

[0041] P cha,i (t)·P dis,i (t) = 0 means that charging and discharging will not occur simultaneously.

[0042] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, considering the access of users in the region to photovoltaic and wind power, the specific constraints are as follows:

[0043] Photovoltaic output:

[0044]

[0045] Photovoltaic output is related to light intensity and temperature. The photovoltaic daily power curve shows the impact of light intensity on photovoltaic output. PV,e is the photovoltaic rated power, t s t0 and t1 are the time of sunrise and sunset respectively; t e1 ,t e2 is the moment corresponding to the maximum photovoltaic output power;

[0046] Wind power output:

[0047]

[0048] Where: ν t is the actual wind speed of the wind turbine, P wtr is the rated power of the wind turbine; ν in , ν out and ν e They are fan cut-in, cut-out and rated wind speed respectively.

[0049] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, the energy storage regulation strategy considering economy and stability is constructed as follows:

[0050] The objective function is to minimize the daily operating cost of the user group connected to the shared energy storage power station in the region. The optimization variables include the capacity of the power station, the maximum charging and discharging power, and the charging and discharging status and power of the energy storage power station and users at different time periods.

[0051] minCost = Cost p + Cost s (twenty one)

[0052]

[0053] Where: Cost is the daily operating cost of the regional user group, Cost p Cost is the cost of purchasing electricity from the grid.s is the service fee paid to the shared energy storage power station, N is the number of regional users, T is the number of time periods in a cycle, σ p (t) is the electricity purchase price in different periods, as shown in Table 1, σ s (t) is the service fee paid by the user to the energy storage power station, which is 0.3 yuan / kWh, P purchase,i (t) is the power purchased by the i-th user in period t, P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i (t) is the discharge power of the energy storage power station to the i-th user in period t.

[0054] In the above-mentioned distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, model verification is also included. The model verification includes:

[0055] a. Constraint simplification;

[0056] b. Preliminary planning results, including:

[0057] The specific method of constraint simplification is: using the Big-M method, nonlinear constraints are transformed into linear constraints, as shown in formula (13), where M is an infinite constant.

[0058]

[0059] After simplification, the original problem is transformed into a mixed integer linear programming problem, which is a convex problem. We choose to use the CPLEX solver in MATLAB to solve it.

[0060] The beneficial effects of the present invention are:

[0061] (1) Accurate modeling of renewable energy generation and load constraints: By building various probability models and considering multiple aspects, it helps to solve problems in the system.

[0062] (2) Improve system economy and stability: This invention establishes an optimal scheduling model for shared energy storage power stations by comprehensively considering the volatility of renewable energy generation, load demand and electricity market prices, and optimizes the system with the goal of minimizing regional daily operating costs. Compared with existing solutions that only plan from the perspective of power generation uncertainty, this invention significantly enhances the stability of the power system while improving system economy.

[0063] (3) Efficient energy storage control strategy: The present invention proposes an efficient energy storage control strategy by optimizing the charging and discharging power, capacity and other parameters of the energy storage power station. Compared with the problem of complex algorithms and long solution time in the prior art, the present invention adopts the mixed integer linear programming method to simplify the constraints while ensuring the solution speed, so that the control strategy can effectively achieve economy and ensure the reliability of system operation.

[0064] (4) Promote the integration of renewable energy power generation and the power grid: Through this optimization scheduling strategy, the application of renewable energy power generation can be guided while ensuring the safety and stability of the power grid, and the deep integration of renewable energy power generation and the power system can be achieved, fully tapping the potential of renewable energy power generation and its role in the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 are the distributed generation output and daily load data of different users: (a) user A, (b) user B, (c) user C;

[0066] Figure 2 yes Figure 2 User A optimizes the scheduling curve;

[0067] Figure 3 User B optimizes the scheduling curve;

[0068] Figure 4 User C optimizes the scheduling curve;

[0069] Figure 5 Shared energy storage power station charging and discharging power and power status. DETAILED DESCRIPTION

[0070] The present invention will be further described below in conjunction with the accompanying drawings.

[0071] The present invention provides a distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, including model construction, specifically including the following:

[0072] a. Constraint set integrating multiple requirements:

[0073] (1) Power flow constraints of distribution networks involving multiple nodes;

[0074] (2) Constraints on charging and discharging power of energy storage power stations;

[0075] (2) Regional charging and discharging power balance constraints;

[0076] (3) Continuity constraints on energy storage power station capacity;

[0077] (5) Charging and discharging power constraints of user groups;

[0078] (6) Consider the access of users in the region to photovoltaic and wind power;

[0079] b. Construct energy storage control strategies based on economy and stability.

[0080] Each is described in detail below.

[0081] a. Constraint set that integrates multiple requirements

[0082] (1) The power flow constraints of the distribution network involving multiple nodes are:

[0083]

[0084] Where P i and Q i is the active and reactive power injected at node i, U i , U j is the voltage amplitude of nodes i and j, G i,j , B i,j and θ i,j are the conductance, susceptance and voltage-current phase difference between nodes i and j respectively.

[0085] Since it is a local shared energy storage within the region, the distance between the user and the energy storage is small, and the transmission power and transmission voltage are not high, so the transmission loss of the system can be ignored.

[0086] (2) Constraints on charging and discharging power of energy storage power stations:

[0087]

[0088] Where P cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage power station,

[0089] P cha (t)·P dis (t) = 0 means that charging and discharging will not occur simultaneously.

[0090] (3) Regional charging and discharging power balance constraints:

[0091]

[0092] Since transmission losses are ignored, the difference in charging and discharging power among users in the region is equal to the difference in charging and discharging power of the shared energy storage power station.

[0093] (4) Continuity constraints on energy storage power station capacity:

[0094]

[0095] Where E(t) is the state of charge of the shared energy storage power station, E min and Emax are the minimum and maximum values ​​of charge, P cha (t) and P dis (t) are the charge and discharge power, η cha and η dis They are the charging and discharging efficiency of the energy storage power station.

[0096] (5) User group power balance constraints:

[0097] P purchase,i (t)+P dis,i (t)+P PV,i (t)+P wind,i (t) = P cha,i (t)+P load,i (t) (29)

[0098] Where P PV,i (t) is the photovoltaic output of the i-th user, P wind,i (t) is the wind power output of the ith user, P load,i (t) is the load of the ith user.

[0099] (6) User group charging and discharging power constraints:

[0100]

[0101] In the formula The maximum charging power of the user to the energy storage power station, is the maximum discharge power, P cha,i (t)·P dis,i (t) = 0 means that charging and discharging will not occur simultaneously.

[0102] (7) Considering the access of users in the region to photovoltaic and wind power, the specific constraints are as follows:

[0103] Photovoltaic output:

[0104]

[0105] Photovoltaic output is related to light intensity and temperature. The photovoltaic daily power curve shows the impact of light intensity on photovoltaic output. PV,e is the photovoltaic rated power, t s t0 and t1 are the time of sunrise and sunset respectively; t e1 ,t e2 is the moment corresponding to the maximum photovoltaic output power.

[0106] Wind power output:

[0107]

[0108] The output of wind power is related to the wind speed. Formula (13) is the relationship between the output power of wind power and wind speed, where P wtr is the rated power of the wind turbine; ν in , ν out and ν e They are fan cut-in, cut-out and rated wind speed respectively.

[0109] b. Construction of energy storage control strategy considering economy and stability

[0110] In order to fully absorb the output of renewable energy power generation and improve the stability of the power grid, energy storage should be used to improve the stability of the power grid. When the distributed power source has redundant power, it charges the energy storage power station. When the distributed power source has insufficient power, the energy storage power station discharges to the outside. At the same time, when the electricity price is low, it gives priority to purchasing electricity from the power grid. The optimization scheduling model is as follows. The objective function is to minimize the daily operating cost of the user group connected to the shared energy storage power station in the region. The optimization variables include the capacity of the power station, the maximum charging and discharging power, and the charging and discharging status and power of the energy storage power station and users in each period.

[0111] minCost = Cost p + Cost s (33)

[0112]

[0113] Where Cost is the daily operating cost of the regional user group. p Cost is the cost of purchasing electricity from the grid. s is the service fee paid to the shared energy storage power station. N is the number of regional users, T is the number of time periods in a cycle, σ p (t) is the electricity purchase price in different periods, as shown in Table 1, σ s (t) is the service fee paid by the user to the energy storage power station, which is 0.3 yuan / kWh, P purchase,i (t) is the power purchased by the i-th user in period t, P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i (t) is the discharge power of the energy storage power station to the i-th user in period t.

[0114] Table 1 Electricity purchase price at different time periods

[0115]

[0116]

[0117] (3) Model verification

[0118] a. Specific methods of constraint simplification

[0119] The above objective function and constraints have established a complete regional shared energy storage power station optimization scheduling model. In order to speed up the solution and reduce the difficulty of solution, the model is simplified while ensuring the rationality of the model. It can be seen that the energy storage power station and user charging and discharging power constraints expressed by equations (2) and (6) are nonlinear. Through the BigM method, the nonlinear constraints are converted into linear constraints, such as equation (13), where M is an infinite constant.

[0120]

[0121] After simplification, the original problem is transformed into a mixed integer linear programming problem, which is a convex problem and can be solved using the CPLEX solver in MATLAB.

[0122] b. Preliminary planning results

[0123] Three users are randomly generated to simulate all user groups in the area. User A is connected to photovoltaic power generation, user B is connected to wind power generation, and user C is connected to both photovoltaic and wind power generation. The photovoltaic daily output data uses typical values, and the wind power output and the daily loads of the three users are randomly generated. The specific data are as follows: Figure 1 Show. Figure 1 Distributed generation output and daily load data of different users: (a) User A, (b) User B, (c) User C.

[0124] The initial state of charge of the shared energy storage power station is selected as 0.2, the maximum and minimum states of charge are 0.95 and 0.05 respectively, and the maximum charge and discharge power is set to 1200kW.

[0125] The optimal dispatch obtained after three users access the shared energy storage power station is as follows: Figure 2-4 As shown in the figure, the line graph is the load and distributed power output data read by MATLAB, and the bar graph is the charging and discharging status and power of the shared energy storage power station used by different users at different times, as well as the power purchased from the power grid.

[0126] Under the optimization of this model, the optimal capacity of the regional shared energy storage power station is 4566kWh, the maximum charging and discharging power of the energy storage power station is 878kW, and the intraday dispatch curve of the shared energy storage power station is as follows: Figure 5 shown.

[0127] from Figure 5It can be seen that during the period from 00:00 to 08:00, the power exchange between the shared energy storage power station and the outside world is almost zero, that is, the power remains unchanged; during the period from 08:00 to 10:00 and from 17:00 to 24:00, the energy storage power station discharges to the outside, the power of the power station decreases, and the maximum discharge power is 878kW, at 13:00; during the period from 11:00 to 16:00, the photovoltaic output arranged at the user's site occupies a dominant position. At this time, the distributed power source charges the shared energy storage power station, the power of the power station increases, and the maximum charging power is 570kW.

[0128] The initial power of the shared energy storage power station is 913kWh, which meets the assumption that the initial state of charge is 0.2 times the energy storage power; the power of the power station reaches the lowest value of 250kWh at 10:00 and the highest value of 3500kWh at 15:00, which meets the assumptions of a minimum energy storage capacity of 228kWh and a maximum energy storage capacity of 4337kWh.

[0129] As can be seen from the above figure, the power loads of users A, B, and C can all be met, and the photovoltaic and wind power outputs are not discarded, achieving maximum economic benefits. The charging and discharging power and energy storage capacity of the regional shared energy storage power station are within the set range and do not cross the limit. At the same time, after one day of operation, the energy storage power station will return to the initial operating state to meet the normal scheduling operation in the region on the next day.

[0130] The results of this example verify the rationality and effectiveness of the power balancing strategy for distributed units based on energy storage regulation.

[0131] In summary, the present invention proposes a storage control strategy that takes into account the access of distributed units. It is a new method for absorbing new energy power generation and can solve the volatility balance problem of power grids with high penetration rates of new energy. Unlike traditional methods that discuss economy / safety alone, the present invention considers the access of renewable energy on the basis of power constraints, energy storage capacity constraints, etc., builds a shared energy storage power station optimization scheduling model, and takes the lowest regional daily operating cost as the objective function to perform mixed linear integer optimization. The effectiveness of the model was verified by three randomly generated users, proving the economy of the shared energy storage power station. In addition, the present invention aims to solve the power system expansion planning problem more quickly and accurately by selecting appropriate methods to simplify constraints, thereby improving the stability and economy of the power grid. The present invention provides a new comprehensive perspective and effective tools to better utilize new energy power generation resources and respond to the requirements of power market reform.

[0132] The present invention is described in detail above in conjunction with the embodiments of the accompanying drawings. A person skilled in the art can make various variations of the present invention according to the above description. Therefore, certain details in the embodiments should not constitute a limitation of the present invention, and the scope of protection of the present invention shall be defined by the scope of the attached claims.

Claims

1. A distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation, including model construction, characterized in that: The details include: a. Constraint set integrating multiple requirements: (1) Power flow constraints of distribution networks involving multiple nodes; (2) Constraints on charging and discharging power of energy storage power stations; (3) Regional charging and discharging power balance constraints; (4) Continuity constraints on energy storage power station capacity; (5) Charging and discharging power constraints of user groups; (6) Consider the access of users in the region to photovoltaic and wind power; b. Construct energy storage control strategies based on economy and stability.

2. A distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1, characterized in that: The power flow constraints of the distribution network involving multiple nodes are: Where: P i and Q i is the active and reactive power injected at node i, U i , U j is the voltage amplitude of nodes i and j, G i,j , B i,j and θ i,j are the conductance, susceptance and voltage-current phase difference between nodes i and j respectively.

3. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: Energy storage power station charging and discharging power constraints: Where: P cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage station, P cha (t)·P dis (t) = 0 means that charging and discharging will not occur simultaneously.

4. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: Regional charging and discharging power balance constraints: Where: P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i P (t) is the discharge power of the energy storage power station to the i-th user in period t, and N is the number of users in the area. cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage power station. Ignoring transmission losses, the difference in charging and discharging power among users in the region is equal to the difference in charging and discharging power of the shared energy storage power station.

5. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: Energy storage power station capacity continuity constraints: Where: E(t) is the state of charge of the shared energy storage power station, E min and E max are the minimum and maximum values ​​of charge, P cha (t) and P dis (t) are the charge and discharge power, η cha and η dis They are the charging and discharging efficiency of the energy storage power station.

6. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: User group power balance constraints: P purchase,i (t)+P dis,i (t)+P PV,i (t)+P wind,i (t)=P cha,i (t)+P load,i (t) (5) Where: P purchase,i (t) is the power purchased by the i-th user in period t, P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i (t) is the discharge power of the energy storage power station to the i-th user in period t. P PV,i (t) is the photovoltaic output of the i-th user, P wind,i (t) is the wind power output of the ith user, P load,i (t) is the load of the ith user.

7. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: User group charging and discharging power constraints: Where: P cha (t) is the charging power of the shared energy storage power station, P dis (t) is the discharge power of the energy storage power station, The maximum charging power of the user to the energy storage power station, is the maximum discharge power, P cha,i (t)·P dis,i (t) = 0 means that charging and discharging will not occur simultaneously.

8. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: Considering that users in the region access photovoltaic and wind power, the specific constraints are as follows: Photovoltaic output: Photovoltaic output is related to light intensity and temperature. The photovoltaic daily power curve shows the impact of light intensity on photovoltaic output. PV,e is the photovoltaic rated power, t s t0 and t1 are the time of sunrise and sunset respectively; t e1 ,t e2 is the moment corresponding to the maximum photovoltaic output power; Wind power output: Where: t is the actual wind speed of the wind turbine, P wtr is the rated power of the wind turbine; ν in , ν out and ν e They are fan cut-in, cut-out and rated wind speed respectively.

9. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1 is characterized by: The energy storage control strategy considering economy and stability is constructed as follows: The objective function is to minimize the daily operating cost of the user group connected to the shared energy storage power station in the region. The optimization variables include the capacity of the power station, the maximum charging and discharging power, and the charging and discharging status and power of the energy storage power station and users at different time periods. minCost = Cost p + Cost s (9) Where: Cost is the daily operating cost of the regional user group, Cost p Cost is the cost of purchasing electricity from the grid. s is the service fee paid to the shared energy storage power station, N is the number of regional users, T is the number of time periods in a cycle, σ p (t) is the electricity purchase price in different periods, as shown in Table 1, σ s (t) is the service fee paid by the user to the energy storage power station, which is 0.3 yuan / kWh, P purchase,i (t) is the power purchased by the i-th user in period t, P cha,i (t) is the charging power of the i-th user to the energy storage power station during period t, P dis,i (t) is the discharge power of the energy storage power station to the i-th user in period t.

10. The distributed photovoltaic, wind power and load power balancing strategy using energy storage regulation according to claim 1, characterized in that: It also includes model verification, which includes: a. Constraint simplification; b. Preliminary planning results, including: The specific method of constraint simplification is: using the Big-M method, nonlinear constraints are transformed into linear constraints, as shown in formula (13), where M is an infinite constant. After simplification, the original problem is transformed into a mixed integer linear programming problem, which is a convex problem. We choose to use the CPLEX solver in MATLAB to solve it.

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